Toward More Intelligent Collaboration: Implementing Data in Partnerships

There is more healthcare data available now than ever before. We have reached a virtual ‘critical mass’, marking a significant shift in our industry’s approach to data. Previously in clinical development, the focus was on data generation, which led to siloed datasets of varying quality or detail that were fiercely protected in a competitive environment. However, with the proliferation of data and improved access, the emphasis has shifted to the analysis and management of this key resource.

Paralleling this shift is a move away from competitive to collaborative strategies. We’ve seen this in the evolution of more flexible and agile clinical research outsourcing models and a greater openness to strategic partnerships, where data play a key role in supporting collaboration and achieving objectives. In all of these scenarios, effective strategies for data transparency and data literacy, supported by a clear data governance framework, are critical for accountability and overcoming potential roadblocks. A strong foundation allows for more effective data implementation and innovative tools to optimize partnerships and joint clinical development programs.

Ensuring Transparency and Aligning on Literacy

As the complexity of clinical development and outsourcing models increases, pharma companies and CROs are investing in and adopting strategies to support formal partnerships and drive cultural alignment. This includes establishing and maintaining a shared digital framework to ensure transparency, measure performance metrics and evaluate objectives. The success of these strategies depends on the transparency of shared data and clarity around data definitions, inputs, and actions.

Transparency requires accurate, real-time, easily accessible data that are clearly defined. There are a wide range of definitions used across the industry to discuss and distinguish clinical development data and these can vary between organizations. While we have yet to settle on industry-wide definitions, it is imperative within a partnership to ensure each data point and metric is clear and understandable to all partners, both in the application and how it relates to other metrics, actions, and the overall strategy.

Ensuring alignment in definitions of both data and metrics is part of a wider consideration for improved data and metrics literacy. Data literacy should be part of an ongoing development program within and across teams to ensure the right data and information are used by the right people at the right time to drive appropriate operational actions and decisions. With the recent data processing innovations within artificial intelligence (AI), we have invested in an enterprise-wide AI literacy program to ensure all our teams share a consistent baseline to enable transformational implementation of these innovations for sponsors.

When teams are aligned on definitions and prioritize transparency and shared access to real-time data, they are better able to track meaningful, actionable metrics. They can more easily drive change and course-correct with collaborative problem-solving because they are working from a shared perspective.

Driving Action with Intelligent Governance

The trust built through transparency is essential to high-performing partnerships. Backing this cultural alignment with formal governance can ensure shared success and mitigate potential risks. Governance that clearly defines roles, responsibilities, and metrics can streamline operations and refine control across organizations, which translates into time and cost efficiencies.

Accurate data is a foundational building block for developing a trusted partnership. By leveraging innovations in automation partners can reduce data errors and increase data quality. For example, automation can rapidly identify missing, incomplete, or incongruous data and immediately flag potential issues for corrective action. Clearly defined roles, responsibilities, and escalation pathways are crucial to ensure rapid actionabouto both data quality and the metrics generated from these data. Over time, we can extrapolate automated data analysis to identify larger patterns within a CTMS or other systems, leading to a higher-level analysis for quality control and action-driven insights across systems and teams.

Accurately Measuring Success

Selecting the right metrics is key to translating data into actionable insight. Dialing into the best-fit metrics can become complicated when so much data is available, which is why establishing a foundation of shared definitions, expectations, and responsibilities is essential. This enhances cross-organizational understanding of the metrics that truly drive change. All partners are able to understand their role in relation to the metric and clearly see the impact of their actions on a project, team, and program level.

Measuring partnership success with meaningful metrics can benchmark progress and offer more control. At ICON, we leverage four foundational categories for meaningful metrics that produce robust and actionable insights: finance, quality, operations, and relationship health. Individual metrics within these categories can flex to match the inherent complexity as outsourcing models evolve and as strategic partnerships deepen. Ideally, continuous direct access to operational data would be available in multiple forms through a single interface, allowing for views of unfiltered data or custom analytics to support strategic decision-making and clearly illustrate the correlation between strategy and operational achievements.

Expanding Innovation to Leverage Potential

Digital innovations, including AI and Machine Learning (ML), are creating new ways to ingest, manage, and interpret data for faster insights that inform the clinical development process. These tools offer opportunities to not just measure success but drive it – enabling efficiencies at various stages and across organizations.

AI is utilized to optimize our partnerships in multiple ways. For example, ICON’s One Search tool optimizes the site identification and selection process. It improves timelines, quality, and performance across key metrics by identifying patterns across a wide network of sites to prioritize the best-fit options for a certain study or program. We can also generate realistic and reflexive targets for patient enrollment with real-time data that allows for strategic adjustments and resource reallocation as needed to mitigate potential risks to target achievement or timelines.

AI innovation is also being used more predictively to identify clearer long-term objectives and align organizations around them for more efficient end-to-end. Cassandra is an ML technology trained on FDA and EMA post-marketing requirements and commitments databases to predict the probability that regulators will require post-marketing studies for the therapy. This offers invaluable insight into long-term strategy and facilitates a more robust mitigation strategy early in the drug development cycle, defining objectives and pathways to achievement.

Foundations for Future Success

Data is abundant and use cases are expanding as pharma, CROs and other stakeholders collaborate to optimize its potential. Shared frameworks, transparency, and common data literacy are effectively addressing challenges as the industry incorporates and defines more data and methodologies evolve to be increasingly cooperative and integrative. Intelligent governance will continue to support accurate measurement and actionable insight while serving as a strong foundation to leverage novel innovations to improve study and partnership success.

Author Details 

Debbie Gilmore, Vice President, Strategic Alliance Management- ICON; Ruth Lalor, Vice President, Business Operations- ICON; Robert Ellison, Vice President, Data & Applied Analytics- ICON

Publication Details